Lung cancer is the leading cause of cancer death accounting for 30% of all male cancer deaths and 26% of all female cancer deaths in US in 2009 [1]. Bronchoscopy is a minimally invasive medical procedure used for staging lung cancer [2]. The standard lung-cancer assessment process involves two stages: 1) planning; and 2) live bronchoscopy [3]. During planning, the physician identifies diagnostic ROIs, such as lymph nodes and nodules using two-dimensional transverse slices from the three-dimensional (3D) multi-detector computed tomography (MDCT) chest scan of the patient. In this stage, the physician also mentally plans a 3D route through the airways to each ROI. Next, during bronchoscopy, the physician mentally registers the bronchoscope position in the 3D MDCT space using the video stream obtained from the bronchoscope. This manual approach to lung-cancer assessment, involving mental path- planning and registration, is well-known to be very difficult, resulting in large skill variations between physicians; also this approach results in navigation errors as early as the second airway generation [4,5].
Electromagnetic (EM) and image-based bronchoscopy guidance systems have been proposed to mitigate the navigational problems of standard bronchoscopy and to enable more effective bronchoscopy procedures [6-16]. EM-based bronchoscopy guidance systems consist of: a) a steerable EM sensor; b) an EM field generator; and c) guidance software [6,8-10,16]. The EM field generator is used to generate an EM field around the patient's chest. The steerable EM sensor, inserted through the bronchoscope's working channel to its tip, is tracked as it is maneuvered through the airways in the external EM field. Prior to the bronchoscopy procedure, the co-ordinate space of the EM field and the 3D MDCT co-ordinate space are synchronized. Thus, as the bronchoscope is moved through the airways, its global position in the MDCT co-ordinate space is nominally known. However, EM-based bronchoscopy guidance systems suffer from localization errors due to the patient's breathing motion; they also need considerable special hardware and are susceptible to local EM-field distortions [11, 12].
Image-based bronchoscopy guidance systems rely on volume-rendered or surface-rendered virtual bronchoscopic (VB) images of the endoluminal airway obtained from the 3D MDCT image [7,13-15,17-21]. During a procedure, the VB images are compared with the real bronchoscopic (RB) video frames, obtained from the camera at the tip of the bronchoscope, to establish the position of the bronchoscope in the airway tree. In the past, we proposed an image-based bronchoscopy guidance system that relied on discrete local registrations at consecutive bifurcations to guide the physician toward the ROI [13, 15]. This system has been extensively validated in live bronchoscopy and was found to suffer from the following limitations: 1) an attending technician must carefully follow the bronchoscope position; 2) it is unable to detect and correct faulty bronchoscope maneuvers, especially when the bronchoscope is advanced across multiple bifurcations; and 3) a re-synchronization procedure has to be followed after adverse events such as patient coughing.